Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions

Fog is a critical external factor that threatens traffic safety on freeways. Variable speed limit (VSL) control can effectively harmonize vehicle speed and improve safety. However, most existing weather-related VSL controllers are limited to adapt to the dynamic traffic environment. This study developed optimal VSL control strategy under fog conditions with fully consideration of factors that affect traffic safety risks. The crash risk under fog conditions was estimated using a crash risk prediction model based on Bayesian logistic regression. The traffic flow with VSL control was simulated by a modified cell transmission model (MCTM). The optimal factors of VSL control were obtained by solving an optimization problem that coordinated safety and mobility with the help of the genetic algorithm. An example of I-405 in California, USA was designed to simulate and evaluate the effects of the proposed VSL control strategy. The optimal VSL control factors under fog conditions were compared with sunny conditions, and different placements of VSL signs were evaluated. Results showed that the optimal VSL control strategy under fog conditions changed the speed limit more cautiously. The VSL control under fog conditions in this study effectively reduced crash risks without significantly increasing travel time, which is up to 37.15% reduction of risks and only 0.48% increase of total travel time. The proposed VSL control strategy is expected to be of great use in the development of VSL systems to enhance freeway safety under fog conditions

Deep Convolutional Network on Point Clouds for 3D Scene Understanding

As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in 2D images. Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. To address this problem, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build deep convolutional networks. We treat convolution ker- nels as nonlinear functions of the local coordinates of 3D points comprised of weight and density functions. With respect to a given point, the weight functions are learned with multi-layer perceptron networks, and density functions through kernel density estima- tion. The most important contribution of this work is a novel reformulation proposed for efficiently computing the weight functions, which allowed us to dramatically scale up the network and significantly improve its performance. The learned convolution kernel can be used to compute translation-invariant and permutation-invariant convolution on any point set in the 3D space. The proposed PointConv have opened doors to new 3D-centric approaches to scene understanding. We show how we can adapt and apply PointConv to an important perception problem in robotics: 3D scene flow estimation. We propose a novel end-to- end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume, upsampling, and warping layers to efficiently handle 3D point cloud data. Unlike traditional cost volumes that require exhaustively computing all the cost values on a high-dimensional grid, our point-based formulation discretizes the cost volume onto input 3D points, and a PointConv operation efficiently computes convolutions on the cost volume. Finally, inspired by the recent development of Transformer, We introduce PointCon- vFormer, a novel building block for point cloud based deep neural network architectures. PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers where the attention computation takes the features into account. In our proposed new operation, feature difference between points in the neighborhood serves as an indicator to re-weight the convolutional weights. Hence, we preserved some of the translation-invariance of the convolution operation whereas taken attention into account to choose the relevant points for convolution. We also explore multi-head mechanisms as well. To validate the effectiveness of PointCon- vFormer, we experiment on both semantic segmentation and scene flow estimation tasks on point clouds with multiple datasets including ScanNet, SemanticKitti, FlyingTh- ings3D and KITTI. Our results show that PointConvFormer substantially outperforms classic convolutions, regular transformers, and voxelized sparse convolution approaches with smaller, more computationally efficient networks

The Value of Combining Wu Ling San Plus and Minus with Repaglinide in the Treatment of Obese Type 2 Diabetes

Objective: To investigate the clinical value and practical effects of the treatment of obese type 2 diabetes mellitus patients with Wu Ling San plus and minus combined with Repaglinide. Methods: Twenty-two obese type 2 diabetic patients attending the outpatient clinic of Yixing Traditional Chinese Medicine Hospital from September 2020 to March 2022 were randomly selected as the subjects of this study, and all of them were divided into treatment group (n=11, Wu Ling San plus and minus + Repaglinide) and control group (n=11, single Repaglinide) according to the computerized random series grouping method. The clinical data and overall efficacy of the two groups were compared. Results: After treatment, the treatment group had better blood glucose, blood lipids and other basic indicators than the control group (P<0.05); all Chinese medicine symptoms scores and complication rates of the treatment group were lower than those of the control group (P<0.05). Conclusion: The treatment of obese type 2 diabetes mellitus patients with Wu Ling San plus reduction + Repaglinide has significant efficacy and high drug safety, and can stabilize many indicators of blood glucose and blood lipids, reduce the risk of complications and control their body weight, which can be promoted and used in the treatment of related clinical conditions

ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.Comment: 0 pages, 5 figure